
MLP. MLP flattens data to one dimension, then traverses \([256,1024,512,256,128,64]\)  fully connected layers each separated by batch normalization and dropout layers. Such a MLP is adapted for MOE estimation in our earlier work [1]}.

CNN. We use six CNN layers with batch normalization, dropout, and maxpool layers between each. The output is flattened into one dimension. Such CNN model is adapted in [2]} for DoA estimation.

SqueezeNet, which uses the same symmetric kernel grouped CNN but lacks residual links. We adapted open source code [3]} [4]} for this task.

RCNN, which is proposed above.

